From the view of computational complexity, the system identification methods proposed in [1]}, [2]}, [3]}, [4]}, [5]}, [6]} are not scalable since the size of the Hankel matrix
increases quadratically with the length of output signal \(T\)  and cubically with the system state dimension \(n\) . The result is the singular value decomposition used in the Ho-Kalman Algorithm cannot be computed. This quadratic/cubic dependence on the problem size greatly limits its application in large scale system identification problems.
